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Scatter Matrix versus the Proposed Distance Matrix on Linear Discriminant Analysis for Image Pattern Recognition

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

In this paper, we explore the performance of Linear Discriminant Analysis (LDA) by replacing the scatter matrix with the distance matrix for image classification. First we present the intuitive arguments for using the distance matrix in LDA. Based on the experiments on face image database, it is observed that the performance in terms of prediction accuracy is better when the distance matrix is used instead of scatter matrix in Linear Discriminant Analysis (LDA) under certain circumstances. Above all, it is observed consistently that the variation of percentage of success with the selection of training set is less when distance matrix is used when compared with the case when scatter matrix is used. The results obtained from the experiments recommend the usage of distance matrix in place of scatter matrix in LDA. The relationship between the scatter matrix and the proposed distance matrix is also deduced.

Keywords

Principal component analysis (PCA) Linear discriminant analysis (LDA) Inner product Similarity measurements 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of Technology TrichyTrichyIndia

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